Comparer des méthodes
Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| Expérience factorielle randomisée en grappes× | Expérience à bras multiples× | |
|---|---|---|
| Domaine | Plans d'expériences | Plans d'expériences |
| Famille | Process / pipeline | Process / pipeline |
| Année d'origine≠ | 1990s (formalized in group-randomized trial literature) | 1990s–2000s (clinical formalization); multi-arm concept implicit in ANOVA-era factorial designs |
| Auteur d'origine≠ | David M. Murray and colleagues; Allan Donner & Neil Klar | Developed within clinical trials methodology; formalized by Parmar, Royston and colleagues (UK MRC CTU, early 2000s) |
| Type | Experimental design | Experimental design |
| Source fondatrice≠ | Murray, D. M. (1998). Design and Analysis of Group-Randomized Trials. Oxford University Press. ISBN: 978-0195120912 | Royston, P., Parmar, M. K. B., & Qian, W. (2003). Novel designs for multi-arm clinical trials with survival outcomes with an application in ovarian cancer. Statistics in Medicine, 22(14), 2239–2256. DOI ↗ |
| Alias | cluster-randomized factorial design, group-randomized factorial trial, CRT factorial, clustered factorial experiment | multi-arm trial, multiple-arm experiment, multi-group experiment, many-arm design |
| Apparentées | 5 | 5 |
| Résumé≠ | A cluster randomized factorial experiment assigns intact groups (clusters such as schools, clinics, or communities) at random to all combinations of two or more treatment factors, enabling simultaneous evaluation of multiple interventions and their interactions while respecting the natural grouping of participants. It merges the logistical and ethical advantages of cluster randomization with the efficiency of factorial design. | A multi-arm experiment simultaneously compares three or more treatment or intervention conditions — each called an arm — against a shared control or against one another. By testing multiple alternatives in a single study, it yields more information per participant than running separate two-group experiments sequentially, while controlling the overall Type I error rate through pre-specified comparison strategies. |
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